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Analyzing outputs from Titan LES

This work will investigate the use of ML to perform denoising of traces and/or E-field reconstruction

A lightweight version of ein aiming at providing only two basic features: open a notebook from within emacs into the browser, and save the notebook with cell output.

Jupyter notebook client in Emacs. Mirror (push) from the ghub repo ein, itself a fork from the original ein.

It's a simulation made up of three different modules which is based on the VD design of the DUNE project.

The first module calculates the drift field inside an anode using perforated PCB technology, and calculates the weighting field on each charge readout view. It generates 2 * (1 + 3) .npy files containing the scalar potential field and each component of the corresponding vector field. The second module uses the Shockley-Ramo theorem to calculate the signal induced on each view. The module needs the previously calculated files to work. The output is a .csv file containing the simulated charge for each electron generated, the drift time and the view on which the charge was collected. We also have a file containing the signals generated for the three charge reading views. The final module simulates electron drift towards the anode plane on a larger volume of liquid argon. Given a channel number, the aim is to visualize waveforms as a function of reconstructed trace angles in the detector.

Translated with DeepL.com (free version)

Skeleton project for FOM contribution